import logging
import math
import urllib.request

import cv2
import numpy
from paddleclas import PaddleClas
from tenacity import retry, stop_after_attempt, wait_random


@retry(stop=stop_after_attempt(3), wait=wait_random(1, 3), reraise=True,
       after=lambda x: logging.warning("获取图片失败！"))
def read(image_path):
    """
    从网络或本地读取图片
    :param image_path: 网络或本地路径
    :return: NumPy数组形式的图片
    """
    if image_path.startswith("http"):
        # 发送HTTP请求并获取图像数据
        resp = urllib.request.urlopen(image_path, timeout=60)
        # 将数据读取为字节流
        image_data = resp.read()
        # 将字节流转换为NumPy数组
        image_np = numpy.frombuffer(image_data, numpy.uint8)
        # 解码NumPy数组为OpenCV图像格式
        image = cv2.imdecode(image_np, cv2.IMREAD_COLOR)
    else:
        image = cv2.imread(image_path)
    return image


def capture(image, rectangle):
    """
    截取图片
    :param image: 图片NumPy数组
    :param rectangle: 要截取的矩形
    :return: 截取之后的图片NumPy
    """
    x1, y1, x2, y2 = rectangle
    return image[int(y1):int(y2), int(x1):int(x2)]


def split(image, ratio=1.414, overlap=0.05):
    """
    分割图片，只分割过长的图片，暂不处理过宽的图片
    :param image:图片，可以是NumPy数组或文件路径
    :param ratio: 分割后的比例
    :param overlap: 图片之间的覆盖比例
    :return: 分割后的图片组(NumPy数组形式)
    """
    split_result = []
    if isinstance(image, str):
        image = read(image)
    # 获取图片的宽度和高度
    height, width = image.shape[:2]
    # 计算宽高比
    img_ratio = height / width
    # 检查是否需要裁剪
    if img_ratio > ratio:
        split_ratio = ratio - overlap
        # 分割后的高度
        new_img_height = width * ratio
        for i in range(math.ceil(height / (width * split_ratio))):
            offset = round(width * split_ratio * i)
            # 参数形式为[y1:y2, x1:x2]
            cropped_img = capture(image, [0, offset, width, offset + new_img_height])
            split_result.append({"img": cropped_img, "x_offset": 0, "y_offset": offset})
    else:
        split_result.append({"img": image, "x_offset": 0, "y_offset": 0})
    return split_result


def parse_rotation_angles(image):
    """
    判断图片旋转角度，逆时针旋转该角度后为正。可能值["0", "90", "180", "270"]
    :param image: 图片NumPy数组或文件路径
    :return: 最有可能的两个角度
    """
    angles = ['0', '90']
    model = PaddleClas(model_name="text_image_orientation")
    clas_result = model.predict(input_data=image)
    try:
        clas_result = next(clas_result)[0]
        if clas_result["scores"][0] < 0.5:
            return angles
        angles = clas_result["label_names"]
    except Exception as e:
        logging.error("获取图片旋转角度失败", exc_info=e)
    return angles


def rotate(image, angle):
    """
    旋转图片
    :param image: 图片NumPy数组
    :param angle: 逆时针旋转角度
    :return: 旋转后的图片NumPy数组
    """
    if angle == 0:
        return image
    height, width = image.shape[:2]
    if angle == 180:
        new_width = width
        new_height = height
    else:
        new_width = height
        new_height = width
    # 绕图像的中心旋转
    # 参数：旋转中心 旋转度数 scale
    matrix = cv2.getRotationMatrix2D((width / 2, height / 2), angle, 1)
    # 旋转后平移
    matrix[0, 2] += (new_width - width) / 2
    matrix[1, 2] += (new_height - height) / 2
    # 参数：原始图像 旋转参数 元素图像宽高
    rotated = cv2.warpAffine(image, matrix, (new_width, new_height))
    return rotated


def invert_rotate_point(point, center, angle):
    """
    反向旋转图片上的点
    :param point: 点
    :param center: 旋转中心
    :param angle: 旋转角度
    :return: 旋转后的点坐标
    """
    matrix = cv2.getRotationMatrix2D(center, angle, 1)
    if angle != 180:
        # 旋转后平移
        matrix[0, 2] += center[1] - center[0]
        matrix[1, 2] += center[0] - center[1]

    reverse_matrix = cv2.invertAffineTransform(matrix)

    point = numpy.array([[point[0]], [point[1]], [1]])
    return numpy.dot(reverse_matrix, point)


def invert_rotate_rectangle(rectangle, center, angle):
    """
    反向旋转图片上的矩形
    :param rectangle: 矩形
    :param center: 旋转中心
    :param angle: 旋转角度
    :return: 旋转后的矩形坐标
    """
    if angle == 0:
        return list(rectangle)

    x1, y1, x2, y2 = rectangle

    # 计算矩形的四个顶点
    top_left = (x1, y1)
    bot_left = (x1, y2)
    top_right = (x2, y1)
    bot_right = (x2, y2)

    # 旋转矩形的四个顶点
    rot_top_left = invert_rotate_point(top_left, center, angle).astype(int)
    rot_bot_left = invert_rotate_point(bot_left, center, angle).astype(int)
    rot_bot_right = invert_rotate_point(bot_right, center, angle).astype(int)
    rot_top_right = invert_rotate_point(top_right, center, angle).astype(int)

    # 找出旋转后矩形的新左上角和右下角坐标
    new_top_left = (min(rot_top_left[0], rot_bot_left[0], rot_bot_right[0], rot_top_right[0]),
                    min(rot_top_left[1], rot_bot_left[1], rot_bot_right[1], rot_top_right[1]))
    new_bot_right = (max(rot_top_left[0], rot_bot_left[0], rot_bot_right[0], rot_top_right[0]),
                     max(rot_top_left[1], rot_bot_left[1], rot_bot_right[1], rot_top_right[1]))

    return [new_top_left[0], new_top_left[1], new_bot_right[0], new_bot_right[1]]


def expand_to_a4_size(image, center=False):
    """
    将图片扩充到a4大小
    :param image: 图片NumPy数组
    :param center: 是否将原图置于中间
    :return: 扩充后的图片NumPy数组和偏移量
    """
    h, w = image.shape[:2]
    offset_x, offset_y = 0, 0
    if h * 1.0 / w >= 1.42:
        exp_w = int(h / 1.414 - w)
        if center:
            offset_x = int(exp_w / 2)
            exp_img = numpy.zeros((h, offset_x, 3), dtype="uint8")
            exp_img.fill(255)
            image = numpy.hstack([exp_img, image, exp_img])
        else:
            exp_img = numpy.zeros((h, exp_w, 3), dtype="uint8")
            exp_img.fill(255)
            image = numpy.hstack([image, exp_img])
    elif h * 1.0 / w <= 1.40:
        exp_h = int(w * 1.414 - h)
        if center:
            offset_y = int(exp_h / 2)
            exp_img = numpy.zeros((offset_y, w, 3), dtype="uint8")
            exp_img.fill(255)
            image = numpy.vstack([exp_img, image, exp_img])
        else:
            exp_img = numpy.zeros((exp_h, w, 3), dtype="uint8")
            exp_img.fill(255)
            image = numpy.vstack([image, exp_img])
    return image, offset_x, offset_y